Radar-based rainfall information has been widely used in hydrological and meteorological applications, as it provides data with a high spatial and temporal resolution that improve rainfall representation. However, the broad diversity of studies makes it difficult to gather a condensed overview of the usefulness and limitations of radar technology and its application in particular situations. In this paper, a comprehensive review through a categorization of radar-related topics aims to provide a general picture of the current state of radar research. First, the importance and impact of the high temporal resolution of weather radar is discussed, followed by the description of quantitative precipitation estimation strategies. Afterwards, the use of radar data in rainfall nowcasting as well as its role in preparation of initial conditions for numerical weather predictions by assimilation is reviewed. Furthermore, the value of radar data in rainfall-runoff models with a focus on flash flood forecasting is documented. Finally, based on this review, conclusions of the most relevant challenges that need to be addressed and recommendations for further research are presented. This review paper supports the exploitation of radar data in its full capacity by providing key insights regarding the possibilities of including radar data in hydrological and meteorological applications.

A set of quality control algorithms developed in the framework of the RADVOL-QC system is described. The algorithms are dedicated to 3-D radar reflectivity data (volumes) provided by a weather radar with single-polarization of the beam. The system consists of two paths of data processing: quality correction and quality characterization. Processing by means of data correction algorithms allows the reduction of uncertainty in the data, whereas quality characterization algorithms generate a map of quality index (QI) that can be attached to the data. The following correction algorithms are included in the proposed scheme: (1) removal of geometrically shaped non-meteorological echoes (from the Sun and other emitters); (2) removal of measurement noise (specks); (3) beam blockage correction, and, (4) attenuation in the rain correction. The list of quality characterization algorithms is longer, as it also includes evaluation of data uncertainty due to technical radar parameters, distance to radar related effects, and ground clutter. All the algorithms have been developed paying special attention to possibility of operational application. The system is planned to be extended successively by other quality factors. Copyright © 2012 Royal Meteorological Society

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

The paper is focused on the problem of weather radar precipitation compositing and aims at producing the data for the whole measurement domain in a way that enables to get a quality of the composite as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated, which apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of QI value and distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques have been compared with standard ones that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques has been performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and presence of contaminations has been carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site has been found the most suitable for radar precipitation compositing.

A System for the Estimation and Nowcasting of Precipitation (SEiNO) is being developed at the Institute of Meteorology and Water Management – National Research Institute. Its aim is to provide the national meteorological and hydrological service with comprehensive operational tools for...

Precipitation estimation models are typically sourced by rain gauges, weather radars and satellite observations. A relatively new technique of precipitation estimation relies on the network of Commercial Microwave Links (CMLs) employed for cellular communication networks: the rain‐inducted attenuation in the links enables the precipitation estimation. In the paper, it is analysed to what extent the precipitation derived from CML attenuation data is useful in estimation of the precipitation field with the high temporal and spatial resolution required in nowcasting models. Two methods of determination of precipitation along CMLs from attenuation of signal with several frequencies were proposed. Then, in order to generate precipitation field, three approaches for assigning appropriate precipitation values to a specific point or set of pixels along the link are developed and tested. The CML‐based estimates are compared with point observations from manual rain gauges and multi‐source precipitation fields using daily and half‐hourly accumulations. It was found that the CML‐based precipitation fields are much worse than radar‐derived estimates. At the same time, they had slightly poorer reliability than spatially interpolated telemetric rain gauge data and significantly higher reliability than satellite estimates. Furthermore, the impact of link characteristics, such as length and frequency, on the reliability of CML‐based precipitation estimates is analysed.